Regulation of localized RNAs - fibroblast cells
CCBR-638
Maggie Cam
Nov 11, 2015
Background
We want to analyze two RNA-Seq datasets derived from total RNA of fractionated Protrusions (Ps) and cell body (CB) fractions of mouse fibroblast cells. We are interested in RNAs that are enriched in protrusions (i.e. have a high Ps/CB ratio) and want to identify subsets of these RNAs that are co-regulated by different factors.
The first dataset aims at identifying RNAs whose enrichment at protrusions is (or is not) affected by knockdown of the APC tumor suppressor protein. We have sequenced 4 control (si-control) and 4 APC knockdown (si-APC) replicates. Each replicate consists of paired Ps and CB fractions (i.e. 16 samples total).
The second dataset aims at identifying RNAs whose enrichment at protrusions is affected by expression of a competing UTR construct, which mislocalizes RNAs through sequestration of necessary factors. We have sequenced 4 control (HBB) and 4 experimental (Pkp4) replicates. Each replicate consists of paired Ps and CB fractions (i.e. 16 samples total).
Data Processing
## Bioconductor version 3.2 (BiocInstaller 1.20.3), ?biocLite for help## A new version of Bioconductor is available after installing the most
## recent version of R; see http://bioconductor.org/installThis part of the program was run on biowulf, and data transferred to a local directory
#Command line version
module load subread
x=$(ls *.bam)
featureCounts -p -T 8 -s 2 -p -t exon -g gene_id -a /data/maggiec/RNASeq/Genomes/mm10/gencode.vM4.all.gtf -o counts_ss.txt $x
#Used R version:
gtf="/data/maggiec/RNASeq/Genomes/mm10/gencode.vM4.all.gtf"
targets <- readTargets()
fc <- featureCounts(files=targets$bam,isGTFAnnotationFile=TRUE,nthreads=32,
annot.ext=gtf,GTF.attrType="gene_name",strandSpecific=2,isPairedEnd=TRUE)
x <- DGEList(counts=fc$counts, genes=fc$annotation)Load data from local directory:
## [1] "fc" "gtf" "targets" "x"## bam Cell Compartment Replicate Phenotype
## 1 Sample_si.APC_CB.1.bam APC CB 1 APC_CB
## 2 Sample_si.APC_CB.2.bam APC CB 2 APC_CB
## 3 Sample_si.APC_CB.3.bam APC CB 3 APC_CB
## 4 Sample_si.APC_CB.4.bam APC CB 4 APC_CB
## 5 Sample_si.APC_Ps.1.bam APC Ps 1 APC_Ps
## 6 Sample_si.APC_Ps.2.bam APC Ps 2 APC_Ps
## 7 Sample_si.APC_Ps.3.bam APC Ps 3 APC_Ps
## 8 Sample_si.APC_Ps.4.bam APC Ps 4 APC_Ps
## 9 Sample_si.control_CB.1.bam Con CB 1 Con_CB
## 10 Sample_si.control_CB.2.bam Con CB 2 Con_CB
## 11 Sample_si.control_CB.3.bam Con CB 3 Con_CB
## 12 Sample_si.control_CB.4.bam Con CB 4 Con_CB
## 13 Sample_si.control_Ps.1.bam Con Ps 1 Con_Ps
## 14 Sample_si.control_Ps.2.bam Con Ps 2 Con_Ps
## 15 Sample_si.control_Ps.3.bam Con Ps 3 Con_Ps
## 16 Sample_si.control_Ps.4.bam Con Ps 4 Con_Ps## Status Sample_si.APC_CB.1.bam
## 1 Assigned 20305088
## 2 Unassigned_Ambiguity 308973
## 3 Unassigned_MultiMapping 3871900
## 4 Unassigned_NoFeatures 840823
## 5 Unassigned_Unmapped 0
## 6 Unassigned_MappingQuality 0
## 7 Unassigned_FragementLength 0
## 8 Unassigned_Chimera 0
## 9 Unassigned_Secondary 0
## 10 Unassigned_Nonjunction 0
## 11 Unassigned_Duplicate 0
## Sample_si.APC_CB.2.bam Sample_si.APC_CB.3.bam Sample_si.APC_CB.4.bam
## 1 17703207 18009731 15908957
## 2 269120 268304 241514
## 3 3428255 3457842 3135614
## 4 739520 834576 686874
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## Sample_si.APC_Ps.1.bam Sample_si.APC_Ps.2.bam Sample_si.APC_Ps.3.bam
## 1 16214065 14963100 19532841
## 2 325964 305871 371835
## 3 5844378 6089572 6446149
## 4 458341 470529 511897
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## Sample_si.APC_Ps.4.bam Sample_si.control_CB.1.bam
## 1 16877618 16310291
## 2 327423 260673
## 3 5711504 3377245
## 4 445550 671136
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## Sample_si.control_CB.2.bam Sample_si.control_CB.3.bam
## 1 16325320 20138987
## 2 264664 301984
## 3 3393474 4098971
## 4 688194 1019531
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## Sample_si.control_CB.4.bam Sample_si.control_Ps.1.bam
## 1 17292738 15542005
## 2 267364 329631
## 3 3438365 6595530
## 4 766768 364223
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## Sample_si.control_Ps.2.bam Sample_si.control_Ps.3.bam
## 1 13471411 17150190
## 2 278749 317132
## 3 5669362 5787571
## 4 365185 402302
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## Sample_si.control_Ps.4.bam
## 1 15704500
## 2 299725
## 3 5436986
## 4 356842
## 5 0
## 6 0
## 7 0
## 8 0
## 9 0
## 10 0
## 11 0Mapping Rate:
QC Check: Look at raw signal distribution and median expression levels
Data is normalized by TMM: filtered number of genes
## [1] 13015 16
Run Voom
## null device
## 1After Normalization
## Warning: package 'rglwidget' was built under R version 3.2.3## Using as id variables
## null device
## 1You must enable Javascript to view this page properly.
Run Similarity Heatmap
Statistical Analysis of Experimental groups (lmFit)
## celltypeAPC_CB celltypeAPC_Ps celltypeCon_CB celltypeCon_Ps
## 1 1 0 0 0
## 2 1 0 0 0
## 3 1 0 0 0
## 4 1 0 0 0
## 5 0 1 0 0
## 6 0 1 0 0
## 7 0 1 0 0
## 8 0 1 0 0
## 9 0 0 1 0
## 10 0 0 1 0
## 11 0 0 1 0
## 12 0 0 1 0
## 13 0 0 0 1
## 14 0 0 0 1
## 15 0 0 0 1
## 16 0 0 0 1
## attr(,"assign")
## [1] 1 1 1 1
## attr(,"contrasts")
## attr(,"contrasts")$celltype
## [1] "contr.treatment"## [1] "APC_CB" "APC_Ps" "Con_CB" "Con_Ps"Volcano Plot
## [1] 11105 16Provenance:
## R version 3.2.1 (2015-06-18)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.5 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] reshape2_1.4.1 d3heatmap_0.6.1.1 plotly_3.6.0
## [4] amap_0.8-14 rglwidget_0.1.1434 HTSFilter_1.8.0
## [7] Biobase_2.30.0 BiocGenerics_0.16.1 IDPmisc_1.1.17
## [10] lattice_0.20-33 reshape_0.8.5 knitr_1.13
## [13] rgl_0.95.1441 ggplot2_2.1.0 edgeR_3.12.1
## [16] limma_3.26.9 Rsubread_1.20.6 BiocInstaller_1.20.3
##
## loaded via a namespace (and not attached):
## [1] viridis_0.3.4 httr_1.2.1
## [3] tidyr_0.5.1 jsonlite_1.0
## [5] splines_3.2.1 assertthat_0.1.0.99
## [7] Formula_1.2-1 shiny_0.13.2
## [9] stats4_3.2.1 latticeExtra_0.6-28
## [11] yaml_2.1.13 RSQLite_1.0.0
## [13] chron_2.3-47 digest_0.6.9
## [15] GenomicRanges_1.22.4 RColorBrewer_1.1-2
## [17] XVector_0.10.0 colorspace_1.2-6
## [19] htmltools_0.3.5 httpuv_1.3.3
## [21] Matrix_1.2-6 plyr_1.8.4
## [23] DESeq2_1.10.1 XML_3.98-1.4
## [25] genefilter_1.52.1 zlibbioc_1.16.0
## [27] xtable_1.8-2 scales_0.4.0
## [29] BiocParallel_1.4.3 tibble_1.1
## [31] annotate_1.48.0 IRanges_2.4.8
## [33] DT_0.1 SummarizedExperiment_1.0.2
## [35] nnet_7.3-12 survival_2.39-5
## [37] magrittr_1.5 mime_0.5
## [39] evaluate_0.9 RcppArmadillo_0.7.200.2.0
## [41] foreign_0.8-66 tools_3.2.1
## [43] data.table_1.9.6 formatR_1.4
## [45] stringr_1.0.0 S4Vectors_0.8.11
## [47] munsell_0.4.3 locfit_1.5-9.1
## [49] cluster_2.0.4 AnnotationDbi_1.32.3
## [51] lambda.r_1.1.9 DESeq_1.20.0
## [53] GenomeInfoDb_1.6.3 futile.logger_1.4.3
## [55] htmlwidgets_0.6 base64enc_0.1-3
## [57] labeling_0.3 rmarkdown_1.0
## [59] gtable_0.2.0 DBI_0.4-1
## [61] markdown_0.7.7 R6_2.1.2
## [63] gridExtra_2.2.1 Hmisc_3.17-4
## [65] futile.options_1.0.0 stringi_1.1.1
## [67] Rcpp_0.12.6 png_0.1-7
## [69] geneplotter_1.48.0 rpart_4.1-10
## [71] acepack_1.3-3.3